Find research datasets worth reusing

opencc-by-4.0Jun 2022View details →

Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19

Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19.

opencc-by-4.0Sep 2022View details →

Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer

Multi-omics profiling of colorectal cancer (CRC) patients and associated patient-derived organoids. Tumor organoids were characterized in steady-state and perturbed using kinase inhibitors.

opencc-by-4.0Jan 2018View details →

Selectivity profiling of multi-kinase inhibitors across the Human Kinome from ChEMBL

Reported is the list of 596 protein kinase pairs, consisting of 141 kinases and selectivity profiles of 10,060 multi-kinase inhibitors found in ChEMBL23 high-confidence data. For each of the reported protein kinase pairs, UniProt IDs defining the kinase forming a pair is provided, as well as the shared inhibitors and their selectivity profiles. For each target within the pair, potency value for each compound is reported as pIC50 value, as well as the absolute potency difference used to assess the selectivity profiles.

opencc-by-4.0Oct 2023View details →

Phosphoglycerate kinase is a central leverage point in Parkinson's Disease driven neuronal metabolic deficits

Although certain drivers of familial Parkinson’s Disease (PD) compromise mitochondrial integrity, whether metabolic deficits underly other idiopathic or genetic origins of PD is unclear. Here, we demonstrate that PGK1, a gene in the PARK12 susceptibility locus,  is rate limiting in neuronal glycolysis and that modestly increasing PGK1 expression significantly boosts  neuronal ATP production kinetics that is sufficient to suppress PARK20-driven synaptic dysfunction. We found that his activity enhancement depends on the molecular chaperone PARK7/DJ-1, whose loss of function significantly disrupts axonal bioenergetics. In-vivo, viral expression of PGK1 confers protection of striatal DA axons against metabolic lesions. These data support the notion that bioenergetic deficits may underpin PD associated pathologies and point to improving neuronal ATP production kinetics as a promising path forward in PD therapeutics.

opencc-by-4.0May 2019View details →

Design, Synthesis and Biological Evaluation of 7-Chloro-9H-pyrimido[4,5-b]indole-based Glycogen synthase kinase-3β inhibitors

The dataset related to the publication: Andreev et al. Design, Synthesis and Biological Evaluation of 7 Chloro-9H-pyrimido[4,5-b]indole-based Glycogen synthase kinase-3β inhibitors. The files include: 1) Movies of the MD simulations of compounds 14b  (Cmpd14b.mpg) and 24 (Cmpd24.mpg). 2) Raw trajectory files of the Desmond MD simulations of the compounds 14b (14b.zip), 24 (24.zip) and 24 (3aR, 7aS) (24_3aR_7aS.zip) (contains out.cms and the full trj files). 3) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2019) of the compounds 14b (Cmpd14b_QMconftauto_output.mae; Cmpd14b_QMconftauto_output.sdf),  24 (Cmpd24_QMconftauto_output.mae; Cmpd24_QMconftauto_output.sdf) and   24 (3aR, 7aS) (Cmpd24_3aR_7aS_QMconftauto_output.mae; Cmpd24_3aR_7aS_QMconftauto_output.sdf) 4) Jaguar pKa calculation (conformations) output-files for compounds 14b, 14c, 14d, 14e, 14f, 14g, 14h, 14i, 14j, 14k, 14l, 14m, 14n pKa.zip (contains conformation in .mae file and Final weighted pKa value in .out file).

opencc-by-4.0Aug 2024View details →

Janus kinase inhibitor functions

Janus kinase (JAK) inhibitors are a class of medications that target specific pathways involved in the immune response and inflammation. JAK inhibitors work by blocking the activity of one or more of the Janus kinase family of enzymes, which are critical mediators in the signaling pathways of various cytokines and growth factors. These pathways are crucial for the development and function of immune cells. By inhibiting JAK enzymes, these drugs can effectively reduce inflammation and modulate immune responses, making them valuable in treating a range of autoimmune and inflammatory diseases, such as rheumatoid arthritis, psoriasis, and inflammatory bowel disease. Understanding the functions and therapeutic potential of JAK inhibitors is key to optimizing treatment strategies for patients with immune-mediated conditions.

opencc-by-4.0Jul 2021View details →

Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis

We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available. Please cite:  https://pubmed.ncbi.nlm.nih.gov/24521231/  https://pubs.acs.org/doi/10.1021/ci400709d

opencc-by-4.0Oct 2021View details →

Mevalonate kinase variants

This dataset describes the effects of several mutations to human mevalonate kinase. The dataset is designed to be used with the Aquaria molecular graphics system (e.g., <a href="https://aquaria.app/Mouse/MVK?zenodo.3632187.V377I">https://aquaria.app/Mouse/MVK?zenodo.3632187.V377I</a> or <a href="https://aquaria.app/Mouse/MVK?zenodo.3632187.Δ91">https://aquaria.app/Mouse/MVK?zenodo.3632187.Δ91</a>).

opencc-by-4.0Oct 2022View details →

Tyrosine-protein kinase Yes controls endothelial junctional plasticity and barrier integrity by regulating VE-cadherin phosphorylation and endocytosis

Abstract Vascular endothelial (VE)-cadherin in endothelial adherens junctions is an essential component of the vascular barrier, critical for tissue homeostasis and implicated in diseases such as cancer and retinopathies. Inhibitors of Src cytoplasmic tyrosine kinase have been applied to suppress VE-cadherin tyrosine phosphorylation and prevent excessive leakage, edema and high interstitial pressure. Here we show that the Src-related Yes tyrosine kinase, rather than Src, is localized at endothelial cell (EC) junctions where it becomes activated in a flow-dependent manner. EC-specific Yes1 deletion suppresses VE-cadherin phosphorylation and arrests VE-cadherin at EC junctions. This is accompanied by loss of EC collective migration and exaggerated agonist-induced macromolecular leakage. Overexpression of Yes1 causes ectopic VE-cadherin phosphorylation, while vascular leakage is unaffected. In contrast, in EC-specific Src-deficiency, VE-cadherin internalization is maintained, and leakage is suppressed. In conclusion, Yes-mediated phosphorylation regulates constitutive VE-cadherin turnover, thereby maintaining endothelial junction plasticity and vascular integrity. Method for retinal EC distribution analysis Chimeric recombination was induced in iSuRe-Cre+ mice at P3 by i.p. injection of tamoxifen (100 µg/mouse, Sigma). Retinas were taken at P7 and P15, immunostained for CD31 and flat-mounted. Images were taken by z-stack tile scanning using a 10X objective on a confocal microscope (Leica SP8). Maximum intensity projection images of whole retinas were used for image segmentation, which was performed with ImageJ resources. The maximum projection of the MbTomato channel threshold was established to distinguish MbTomato+ cells from the background. Outliers with a radius between 0.2-1.0 µm were removed. The CD31 channel (after maximum projection) was used to define the outlines of veins and arteries; the optic nerve was used as a mask to define a referential system. For computational analysis, a bespoken Python-based workflow was employed, accessible on GitHub (https://github.com/wgiese/retina-vein-artery-cs). For every pixel in the image, three numbers were computed (using the mask as referential): (1) distance to the nearest vein (dv), (2) distance to the nearest artery (da) and (3) radial distance to the optic nerve (r). From these measures, the relative distances by ϕv-a = dv/(dv + da) were obtained. The EC distribution was computed by performing the operation for all YFP-positive pixels, which were used as a proxy for EC distribution. A kernel density estimation was used to approximate the underlying EC distribution in the 2D coordinate system spanned by ϕv-a and r.

opencc-zeroJan 2023View details →

Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

opencc-by-4.0Apr 2023View details →

Kinase ChemoGenomic Set (KCGS) v 2.0 data set

Here we briefly describe the latest iteration of our kinase chemogenomic set, progressing toward eventual total kinome coverage. This new edition is called KCGS2.0. Our kinase chemogenomic set (KCGS) comprises well-annotated inhibitors that target kinases with potent activity but have what we consider narrow-spectrum activity across the kinome. Our goal is to continue growing the set until we have one to three inhibitors for each human kinase. When we reach this point, the set can in principle, be used to determine the relevance and/or function of each kinase in the context of interest. Individually each inhibitor is not promiscuous, and each has defined activity on a narrow set of kinases. When the set is screened in disease-relevant phenotypic assays, one can infer kinase vulnerability based on the results and follow up with more detailed experiments on kinases of interest to confirm the hypothesized dependence. We have now added additional compounds to KCGS1.0 and created KCGS2.0 affording expanded breadth (more kinases covered) and depth (additional chemotypes for kinase) of coverage. The set is being distributed through cancertools.org, Cancer Research UK's research tools arm. Follow this link (https://www.cancertools.org/tools ) and search for KCGS at this tools page. Frequently Asked Questions In the summary spreadsheet, what does the S10 (1 mM) mean? S10 (1 mM) is a selectivity metric generated from Discoverx broad kinome screening data. It is the number of kinases with PoC<10 (equivalent to 90%I) divided by the number of wild type (non-mutant) kinases screened (generally 403 kinases here). In our case we screened inhibitors at a concentration of 1 micromolar, thus, the S10 (1 mM). Smaller S10 values represent a more selective compound. Of course, this is an imperfect selectivity measure. Do you have the same data on all the compounds? We don’t. Compounds that ended up in KCGS2.0 but started in PKIS may only have data from the Nanosyn panel of assays we ran at that time. In that PKIS experiment we screened compounds at 100 nM and 1 micromolar. In the spreadsheet of KCGS2.0 summary data, any reference to Nanosyn is talking about the data from the 1 micromolar screening at Nanosyn. Please check out the PKIS paper and supplemental information for more information on the Nanosyn data. Here is the pubmed link: <a href="https://pubmed.ncbi.nlm.nih.gov/26501955/">https://pubmed.ncbi.nlm.nih.gov/26501955/</a> Compounds from PKIS2 that ended up in KCGS2.0 have broad screening data from the Discoverx panel of assays. The work around KCGS is described here: <a href="https://pubmed.ncbi.nlm.nih.gov/33429995/">https://pubmed.ncbi.nlm.nih.gov/33429995/</a>. Please refer to this paper for more detail on the design of KCGS and the use of KCGS. These same guidelines were used in expanding to KCGS2.0; so many of your KCGS2.0 questions may be answered by reading through that paper. The brand-new compound additions that turn KCGS into KCGS2.0 comes with new, and for the most part unpublished, Discoverx kinome scan data. We have added compounds that cover new kinases (increased breadth of coverage) as well as adding new chemotypes for some kinases (increased depth of coverage). Are all the compounds exquisitely selective? Initially we strived for S10 (1 mM) < 0.03 or so. Many of the compounds only had Nanosyn data initially. We have now tested many of those in the kinomescan assay, and that is reflected in the screening column (KCGS2.0 data overview spreadsheet) if it says “Nanosyn, Discoverx”. These two assay panels are different (but with many overlapping kinases) and completely different assay formats. In some cases, testing in the Discoverx panel has surfaced additional kinase targets, meaning that compounds with less-than-ideal selectivity are in the set. This just means users of the set need to take this into account as hits from phenotypic screens are followed up. What are the references you provide in the “reference” column? When we started building kinase chemogenomic sets and designing new kinase inhibitors, our premise was that we could use kinase inhibitors made for one target as starting points for other kinase targets. The "reference" column provides the original med chem references that report a number of these compounds. If one of these compounds hits in your assay, I encourage you to check out the original paper in case it offers any additional insights. Apologies if we have missed some references. This exercise has demonstrated that useful inhibitors for "other", often unrelated, kinases can be identified by broad screening of compounds made in medicinal campaigns for another kinase. If I get a hit from compound do I know with certainty that the target is critical for my phenotype? Screening the set will generate hypotheses for you to explore. Any hit in a phenotypic assay needs to be followed up carefully. Of course, looking at the list of targets in row I (target data: generally, Kd<100 nM and/or %I>90 (screened at 1 mM)) is a great place to start. Remember there COULD be other targets. We have not screened all kinases, for example. In addition, S10 (1 mM) is an imperfect selectivity metric. We have highlighted targets with Kd or IC50 < 100 nM, or with >90%I at 1 uM. Targets just slightly weaker than this could also lead to (or contribute to) a phenoytpe. Of course, there may be a chance a compound binds to a nonkinase target. For hits of interest, ALL possibilities should be considered as you seek to link compound to target to mechanism and phenotype. ---------------------------------------------- For additional information and to leave feedback, click here: <a href="https://openlabnotebooks.org/release-of-the-kinase-chemogenomic-set-2-0-kcgs2-0/">openlabnotebooks</a> ----------------------------------------------

opencc-zeroApr 2023View details →

Stimulation of the catalytic activity of the tyrosine kinase Btk by the adaptor protein Grb2: Part 2

The Tec-family kinase Btk contains a lipid-binding Pleckstrin homology and Tec homology (PH-TH) module connected by a proline-rich linker to a "Src module", an SH3-SH2-kinase unit also found in Src-family kinases and Abl. We showed previously that Btk is activated by PH-TH dimerization, which is triggered on membranes by the phosphatidyl inositol phosphate PIP3, or in solution by inositol hexakisphosphate (IP6) (Wang et al. 2015, https://doi.org/10.7554/eLife.06074). We now report that the ubiquitous adaptor protein growth-factor-receptor-bound protein 2 (Grb2) binds to and substantially increases the activity of PIP3-bound Btk on membranes. Using reconstitution on supported-lipid bilayers, we find that Grb2 can be recruited to membrane-bound Btk through interaction with the proline-rich linker in Btk. This interaction requires intact Grb2, containing both SH3 domains and the SH2 domain, but does not require that the SH2 domain be able to bind phosphorylated tyrosine residues – thus Grb2 bound to Btk is free to interact with scaffold proteins via the SH2 domain. We show that the Grb2-Btk interaction recruits Btk to scaffold-mediated signaling clusters in reconstituted membranes. Our findings indicate that PIP3-mediated dimerization of Btk does not fully activate Btk, and that Btk adopts an autoinhibited state at the membrane that is released by Grb2.

opencc-zeroApr 2023View details →

Stimulation of the catalytic activity of the tyrosine kinase Btk by the adaptor protein Grb2: Part 3

The Tec-family kinase Btk contains a lipid-binding Pleckstrin homology and Tec homology (PH-TH) module connected by a proline-rich linker to a "Src module", an SH3-SH2-kinase unit also found in Src-family kinases and Abl. We showed previously that Btk is activated by PH-TH dimerization, which is triggered on membranes by the phosphatidyl inositol phosphate PIP3, or in solution by inositol hexakisphosphate (IP6) (Wang et al. 2015, https://doi.org/10.7554/eLife.06074). We now report that the ubiquitous adaptor protein growth-factor-receptor-bound protein 2 (Grb2) binds to and substantially increases the activity of PIP3-bound Btk on membranes. Using reconstitution on supported-lipid bilayers, we find that Grb2 can be recruited to membrane-bound Btk through interaction with the proline-rich linker in Btk. This interaction requires intact Grb2, containing both SH3 domains and the SH2 domain, but does not require that the SH2 domain be able to bind phosphorylated tyrosine residues – thus Grb2 bound to Btk is free to interact with scaffold proteins via the SH2 domain. We show that the Grb2-Btk interaction recruits Btk to scaffold-mediated signaling clusters in reconstituted membranes. Our findings indicate that PIP3-mediated dimerization of Btk does not fully activate Btk, and that Btk adopts an autoinhibited state at the membrane that is released by Grb2.

opencc-zeroApr 2023View details →

Stimulation of the catalytic activity of the tyrosine kinase Btk by the adaptor protein Grb2: Part 1

opencc-by-4.0Jun 2023View details →

Predicted models of S receptor kinase ectodomain (eSRK), S-locus protein 11 (SP11), and eSRK-SP11 complexes

Predicted models of S receptor kinase ectodomain (eSRK), S-locus protein 11 (SP11), and their complexes using ColabFold and curated multiple sequence alignments (MSAs).

opencc-by-4.0Jul 2023View details →

Allosteric kinase inhibitors

The deposition contains 262 allosteric human protein kinase inhibitors for which X-ray structures of kinase-inhibitor complexes are available. The deposition updates allosteric kinase inhibitors available in <a href="https://doi.org/10.5281/zenodo.4436775">https://doi.org/10.5281/zenodo.4436775</a>.

ALTA-1L Study: A Study of Brigatinib Versus Crizotinib in Anaplastic Lymphoma Kinase Positive (ALK+) Advanced Non-small Cell Lung Cancer (NSCLC) Participants

ClinicalTrials.gov study NCT02737501. IPD Sharing: YES. Countries: 19. Publications: 5.

Phase 2 Study of Brigatinib in Japanese Participants With Anaplastic Lymphoma Kinase (ALK)-Positive Non-Small Cell Lung Cancer (NSCLC)

ClinicalTrials.gov study NCT03410108. IPD Sharing: YES. Countries: 1. Publications: 2.

A Multicenter, Open-label, Pilot Study of Soticlestat (TAK-935/OV935) in Participants With 15Q Duplication Syndrome (Dup 15q) or Cyclin-Dependent Kinase-Like 5 (CDKL5) Deficiency Disorder (ARCADE STUD

ClinicalTrials.gov study NCT03694275. IPD Sharing: YES. Countries: 1. Publications: 1.

Treatment of CD79B Mutant Relapsed/Refractory Diffuse Large B-Cell Lymphoma With Bruton Tyrosine Kinase Inhibitor Zanubrutinib

ClinicalTrials.gov study NCT05068440. IPD Sharing: YES. Countries: 1. Publications: 0.